ACR DSI Lab pilot site
Current developments in AI to improve patient care are being driven by research happening primarily at institutions with extensive informatics and data science resources — and primarily using single-institution patient data. Yet repeated studies have shown that these deep learning models do not generalize well across institutional differences, such as patient demographics, disease prevalence, scanners, and acquisition settings . Although AI algorithms are more effective when trained on a wide and diverse array of clinical data, sharing data outside an institution is difficult due to patient privacy concerns. With ACR AI-LAB, the intention is to share AI models between institutions leaving patient information to remain on-site at the originating institution. Emory is one of the 7 sites participating to pilot the infrastructure required to create such a federated environment.
For the first part of this pilot, we are testing breast density algorithm brittleness across 10 Emory sites performing screening mammograms. This project has subprojects
- Work on improving the performance of the breast density algorithm to improve robustness across the various Emory sites
- Generate test datasets using phantoms to provide a baseline for testing for algorithm robustness
- Generate synthetic datasets for testing algorithm robustness
- Explore federated learning, differential privacy and split learning across the 7 sites running the ACR AI lab pilot
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